Are data cleanrooms the solution to what IAB CEO David Cohen has called the “slow motion train wreck” addressability? Voices at the IAB will tell you that they have a big role to play.
“The problem with addressability is that once the cookies are gone, and with the loss of the identifiers, about 80% of the addressable market will become an unknown audience, which is why it’s necessary to have privacy-centric consent and a better exchange of consent value,” said Jeffrey Bustos, vice president, measurement, addressability and data at the IAB.
“Everyone talks about first-party data, and that’s very valuable,” he explained, “but most publishers who don’t have a connection, they have about 3-10% of the data from first part of their readership.” First-party data, from the perspective of advertisers wanting to reach relevant audiences and publishers wanting to deliver valuable inventory, is simply not enough.
Why we care. Two years ago, who was talking about data clean rooms? The renewed interest is recent and significant, according to the IAB. DCRs at least have the potential to keep brands in touch with their audiences on the open internet; maintaining the viability of publisher inventories; and to provide sophisticated measurement capabilities.
How data clean rooms can help. DCRs are a type of privacy-enhancing technology that allows data owners (including brands and publishers) to share first-party customer data in a privacy-compliant manner. Cleanrooms are secure spaces where first-party data from a number of sources can be resolved to the same customer profile while that profile remains anonymized.
In other words, a DCR is a kind of Switzerland – a space where a truce is called on competition while first-party data is enriched without compromising privacy.
“The value of a data cleanroom is that a publisher is able to collaborate with a brand across both data sources and the brand is able to understand audience behavior,” Bestos said. For example, a brand selling glasses may know nothing about its customers except basic transactional data – and that they wear glasses. Matching profiles with a publisher’s behavioral data provides enrichment.
“If you’re able to understand behavioral context, you’re able to understand what your customers are reading, what interests them, what their hobbies are,” Bustos said. Armed with this information, a brand has a better idea of the type of content they want to advertise on.
The publisher must have some level of first-party data for the match to take place, although they don’t have a universal requirement for connections like the New York Times. An editor may be able to match only a small percentage of the eyewear supplier’s customers, but if they enjoy reading the sports and arts sections, that at least gives some indication of the audience the supplier should target.
Dig deeper: Why We Care About Data Cleanrooms
What counts as a good match? In his “Data status 2023which focuses almost exclusively on data cleanrooms, there are concerns that the effectiveness of DCR is threatened by low match rates. Average match rates are around 50% (less for some DCR types).
Bustos is keen to put this into context. “When you combine data from a cookie perspective, the match rates are typically around 70%,” he said, so 50% isn’t terrible, although there is room for improvement.
One obstacle is a persistent lack of interoperability between identity solutions — although it exists; LiveRamp’s RampID is interoperable, for example, with The Trade Desk’s UID2.
Nonetheless, Bustos said, “It’s incredibly difficult for publishers. They have a bunch of ID pixels that fire for all these different things. You don’t know which identity provider to use. There is certainly a long way to go to ensure that there is interoperability.
Maintain an open Internet. While DCRs can help solve the addressability problem, they will also help with the challenge of keeping the Internet open. Walled gardens like Facebook have rich troves of proprietary and behavioral data; brands can access these audiences, but with very limited visibility on them.
“The reason CTV is a really valuable proposition for advertisers is that you’re able to identify the user 1:1, which is really powerful,” Bustos said. “Your standard news or editorial editor doesn’t have that. I mean, The New York Times moved on to this and it was incredibly successful for them. In order to compete with walled gardens and streaming services, publishers must offer some degree of addressability – and without relying on cookies.
But DCRs are a heavyweight. Data maturity is an important qualification for getting the most out of a DCR. The IAB report shows that of the brands evaluating or using DCRs, more than 70% have other data-related technologies, such as CDPs and DMPs.
“If you want a data cleanroom,” Bustos explained, “there are a lot of other technology solutions you need to have in place beforehand. You need to make sure you have strong data assets. He also recommends to start by asking what you want to achieve, not what technology would be nice to have. “The first question is, what do you want to achieve? You may not need a DCR. “I want to do this”, then see what tools would get you there. »
Also understand that implementation is going to take talent. “It’s a demanding project in terms of setup,” Bustos said, “and there’s been a significant growth in consulting firms and agencies helping set up these data clean rooms. You need a lot of personnel, so it is more efficient to hire outside help for installation, then just have an in-house maintenance team.
Underutilization of measurement capacity. A key finding of the IAB research is that DCR users leverage the audience matching capabilities far more than they realize the measurement and attribution potential. “You need very strong data scientists and engineers to build advanced models,” Bustos said.
“A lot of brands looking at this question say, ‘I want to be able to do predictive analysis of my high lifetime value customers who are going to buy in the next 90 days.’ Or “I want to be able to measure which channels are driving the biggest uplift. These are very complex analyzes they want to do; but they don’t really have a reason why. For what purpose? Understand your result and develop a sequential data strategy.
Trying to figure out the incremental increase in your marketing can be time-consuming, he warned. “But you can easily do scope, frequency, and overlap analysis.” This will identify unnecessary channel investment and, as a by-product, suggest where the additional uplift is occurring. “Businesses need to know what they want, identify the outcome, and then there are steps that will get you there. This will also help prove ROI.
Dig deeper: Not getting the most out of data cleanrooms is costing marketers money
Get MarTech! Daily. To free. In your inbox.